姚霞 教授

姚霞，教授，博士生导师。围绕我国粮食安全的迫切需求，针对作物生长监测时效性差和作物表型信息高通量获取难的技术瓶颈，聚焦作物长势监测、表型信息获取等关键技术，创新了稻麦表型参数无损感知机理，突破了稻麦表型参数智能解析技术，合作创建作物表型参数精准获取平台，取得了创新性突破，为作物精确栽培和高效育种提供了数字化智能化的新路径。

近年来先后主持国家部/省级项目20多项，在RSE、ISPRS等高水平期刊发表论文70多篇，副主编及参编合作出版专著（教材）6部，出版专著1部；授权国家发明专利22件（其中PCT专利4项），登记国家计算机软件著作权6项；指导研究生近30名。获测绘科学技术奖、南京农业大学研究生教育优秀导师团队、地理信息科技进步奖、日内瓦国际发明展银奖、神农中华农业科技奖优秀创新团队奖、江苏省科技进步一等奖和国家科技进步二等奖。入选江苏省高校青蓝工程中青年学术带头人称号，江苏省第六期333高层次人才培养工程，南京农业大学钟山青年骨干、南京农业大学优秀教师等。

部分论著：

1.专著： 作物生长光谱监测. 科学出版社. 2020.（副主编）

2.专著： Hyperspectral remote sensing of leaf nitrogen concentration in cereal crops. In P. S. Thenkabail, J. Lyon, & A. Huete (Eds.), Hyperspectral Remote Sensing of Vegetation, Second Edition (pp. 163-182). Boca Raton, FL: CRC Press.2018.（参编）

3.专著：物联网与食品质量安全. 科学出版社. 2014.（参编）

4.专著： Estimating leaf nitrogen concentration of cereal crops with hyperspectral data. In: Prasad ST, John GL, Alfredo H. (eds.)  Hyperspectral Remote Sensing of Vegetation. CRC Press, FL, USA. 2011.187-206. （参编）

5.专著：数字农作技术. 科学出版社. 2008.（参编）

发表论文：

Li, W., Li, D., Timothy, A. W., Liu, S., Frédéric, B., Yang, P., Jiang, J., Dong, M., Cheng, T., Zhu, Y*., Cao, W., Yao, X.*, 2025. Improved generality of wheat green LAI models through mitigation of the effect of leaf chlorophyll content variation with red edge vegetation indices. Remote Sensing of Environment. 318: 114589.

Guo, T., Wang, Y., Gu, Y., Fang, Y., Zheng, H., Zhang, X.*, Zhou, D, Jiang， C, Cheng, T, Zhu, Y, Cao, W, Yao, X.*, 2025. MSCVI: An improved algorithm for mitigating LiDAR noise and occlusion effects in field wheat tiller number calculation. Computers and Electronics in Agriculture. 229: 109757.

Guo, T., Mathias, D., Wang, Y., Zheng, H., Zhou, D., Jiang, C.*, Cheng, T., Zhu, Y., Cao, W., Yao, X.*, Evaluating the effects of sampling design and voxel size on wheat green area index estimation using 3D radiative transfer simulations and TLS measurements. IEEE Transactions on Geoscience and Remote Sensing. 2025, 15:23-38

Zhou, M., Zhu, J., Ai, H., Zhang, Y., Timothy, A. W., Zheng, H., Jiang, C., Cheng, T., Zhu, Y., Cao, W., Zhang, X., Yao, X.*, A in-seasonal phenology monitoring approach for wheat breeding accessions with time-series RGB imagery by using a combination KNN-CNN-RF model. ISPRS Journal of Photogrammetry and Remote Sensing. 2025, 227:297-315.

Gu,Y., Wang, Y., Wu,Y., Timothy,A. W., Guo,T., Ai,H., Zheng,H., Cheng, T., Zhu, Y., Cao, W., Yao, X.*, 2024.Novel 3D photosynthetic traits derived from the fusion of UAV LiDAR point cloud and multispectral imager yin wheat. Remote Sensing of Environment. 311: 114244.

 Gu,Y., Wang,Y.,Guo,T., Guo,C., Wang,X., Jiang,C., Cheng,T., Zhu,Y.*, Cao,W., Chen,Q.*, Yao X.*, 2024.Assessment of the influence of UAV-borne LiDAR scan angle and flight altitude on the estimation of wheat structural metrics with different leaf angle distributions.Computers and Electronics in Agriculture.  220:108858.

Mustafa,G., Zheng,H., Imran,H. Khan., Zhu,J., Yamg,T., Wang,A., Xue,B., He,C., Jia,H., Li,G., Cheng,T., Cao,W., Zhu,Y., Yao X.*, 2024.Enhancing fusarium head blight detection in wheat crops using hyperspectral indices and machine learning classifiers.Computers and Electronics in Agriculture. 218:108663.

Mustafa,G.,Zheng,H., Liu,Y., Yang,S., Imran,H.Khan., Sarfraz,H., Liu,J.,Wu,W.,Chen,M., Cheng,T., Zhu,Y., Yao,X.*, 2024.Leveraging machine learning to discriminate wheat scab infection levels through hyperspectral reflectance and feature selection methods. European Journal of Agronomy.161: 127372.

Yuan, J., Li, X., Zhou, M., Zheng, H., Yao, Xia.*, 2024. Rapidly count crop seedling emergence based on waveform Method(WM) using drone imagery at the early stage. Computers and Electronics in Agriculture.220: 108867.

Han, X., Zhou, M., Guo, C., Ai, H., Li, T., Li, W., Zhang, X., Chen, Q., Jiang, C., Cheng, T., Zhu, Y., Cao, W., Yao, X.*, 2024. A fully convolutional neural network model combined with a Hough transform to extract crop breeding field plots from UAV images. International Journal of Applied Earth Observation and Geoinformation. 132: 104057.

Wang, Y., Gu, Y., Tang, J., Timothy, A.W., Guo, C., Zheng, H., Fumiki, H., Cheng, T., Zhu, Y., Cao, W., Yao, X.*, 2024. Quantify wheat canopy leaf angle distribution using terrestrial laser scanning data. IEEE Transactions on Geoscience and Remote Sensing. 62:1-15.

Zheng, H., Tang, W., Yang, T., Zhou, M., Guo, C., Cheng, T., Cao, W., Zhu, Y., Zhang, Y., Yao, X.*, 2024. Grain protein content phenotyping in rice via hyperspectral imaging technology and a genome-wide association study. Plant Phenomics. 6: 0200.

Gu, Y., Ai, H., Guo, T., Liu, P., Wang, Y., Zheng, H., Cheng, T., Zhu, Y.*, Cao, W., Yao, X.*, 2023. Comparison of two novel methods for counting wheat ears in the field with terrestrial LiDAR. Plant Methods. 19: 134.

Zhu, J., Yin, Y., Lu, J., Timothy, A.W., Xu, X., Lyu, M., Wang, X., Guo C., Cheng, T., Zhu, Y., Cao, W., Yao, X.*, Zhang, Y., Liu, L., 2023. The relationship between wheat yield and sun-induced chlorophyll fluorescence from continuous measurements over the growing season. Remote Sensing of Environment. 298: 113791.

Li, W., Li, D., Liu, S., Frédéric, B., Ma, Z., He, C., Timothy, A.W., Guo, C., Cheng, T., Zhu, Y.*, Cao, W., Yao, X.*, 2023. RSARE: A physically-based vegetation index for estimating wheat green LAI to mitigate the impact of leaf chlorophyll content and residue-soil background. ISPRS Journal of Photogrammetry and Remote Sensing. 200:138-152.

Zhu, J., Lu, J., Li, W., Wang, Y., Jiang, J., Cheng, T., Zhu, Y., Cao, W., Yao, X.*, 2023. Estimation of canopy water content for wheat through combining radiative transfer model and machine learning. Field Crop Research. 302:109077.

Ma, Z., Li, W., Timothy, A.W., He, C., Wang, X., Zhang, Y., Guo, C., Cheng, T., Zhu, Y., Cao, W., Yao, X.*. 2023. A framework combined stacking ensemble algorithm to classify crop in complex agricultural landscape of high altitude regions with Gaofen-6 imagery and elevation data. International Journal of Applied Earth Observation and Geoinformation. 122:103386.

Mustafa, G., Zheng, H., Li, W., Yin, Y., Wang, Y., Zhou, M., Liu, P., Bilal, M., Jia, H., Li, G., Cheng, T., Tian, Y., Cao, W., Zhu, Y.*, Yao, X.*, 2023. Fusarium head blight monitoring in wheat ears using machine learning and multimodal data from asymptomatic to symptomatic periods. Frontiers in Plant Science. 13:1102341.

Zhou, M., Zheng, H., He, C., Liu, P., Mustafa, G., Wang, X., Cheng, T., Zhu, Y., Cao, W., Yao, X.*, 2023. Wheat phenology detection with the methodology of classification based on the time-series UAV images. Field Crops Research. 292: 108798.

Wang, K., Zhu, J., Xu, X., Li, T., Wang, X., Timothy, A.W., Cheng, T., Zhu, Y*., Cao, W., Yao, X.*, Zhang, Z., 2023. Quantitative monitoring of salt stress in rice with solar-induced chlorophyll fluorescence. European Journal of Agronomy. 150:  126954.

Yin, Y., Zhu, J., Xu, X., Jia, M., Timothy, A.W., Wang, X., Li, T., Cheng, T., Zhu, Y., Cao, W., Yao, X.*, 2023. Tracing the nitrogen nutrient status of crop based on solar-induced chlorophyll fluorescence. European Journal of Agronomy. 149: 126924.

Mustafa, G., Zheng, H., Khan, I., Tian, L., Jia, H., Li, G., Cheng, T., Tian, Y., Cao, W., Zhu, Y.*, Yao, X.*, 2022. Hyperspectral reflectance proxies to diagnose in-field fusarium head blight in wheat with machine learning. Remote Sensing. 14(12): 2784.

Jiang, J., Liu, H., Zhao, C., He, C., Ma, J., Cheng, T., Zhu, Y., Cao, W., Yao, X.*, 2022. Evaluation of diverse convolutional neural networks and training strategies for wheat leaf disease identification with field-acquired photographs. Remote Sensing. 14(14): 3446.

Khan, I.H., Liu, H., Li, W., Cao, A., Wang, X., Liu, H., Cheng, T., Tian, Y., Zhu, Y., Cao, W., Yao, X.*, 2021. Early detection of powdery mildew disease and accurate quantification of its severity using hyperspectral images in Wheat. Remote Sensing. 13, 3612.

Jia, M., Colombo, R., Rossini, M., Celesti, M., Zhu, J., Cogliati, S., Cheng, T., Tian, Y., Zhu, Y., Cao, W., Yao, X.*, 2021. Remote estimation of nitrogen content and photosynthetic nitrogen use efficiency in wheat leaf using sun-induced chlorophyll fluorescence at the leaf and canopy scales. European Journal of Agronomy.12:14.

Jiang, J., Zhu, J., Wang, X., Cheng, T., Tian, Y., Zhu, Y., Cao, W., Yao, X.*, 2021. Estimating the leaf nitrogen content with a new feature extracted from the ultra-high spectral and spatial resolution images in wheat. Remote Sensing. 13, 739.

Zhao, J.,Zhang, X.,Yan, J., Qiu, X.,Yao, X., Zhu, Y., Cao, W.*, 2021. A Wheat Spike Detection Method in UAV Images Based on Improved YOLOv5. Remote Sensing. 10:3390.

Jiang J, Zhu J, Wang X, Cheng T, Tian Y, Zhu Y, Cao W, Yao X*. Estimating the leaf nitrogen content with a new feature extracted from the ultra-high spectral and spatial resolution images in wheat. Remote Sensing. 2021, 13(4): 739.

Jia, M., Colombo, R., Rossini, M., Celesti, M., Zhu, J., Cogliati, S., Cheng, T., Tian, Y., Zhu, Y., Cao, W., Yao, X*. Remote estimation of nitrogen content and photosynthetic nitrogen use efficiency in wheat leaf using sun-induced chlorophyll fluorescence at the leaf and canopy scales. European Journal of Agronomy. 2021.12:14.

Khan I1, Liu H1, Li W, Cao A, Wang X, Liu H, Cheng T, Tian Y, Zhu Y, Cao W, Yao X*. Early detection of powdery mildew disease and accurate quantification of its severity using hyperspectral images in wheat. Remote Sensing. 2021, 13(18): 3612.

Zheng, H., Cheng, T., Zhou, M., Li, D., Yao, X., Tian, Y., Cao, W., Zhu, Y. *, 2019. Improved estimation of rice aboveground biomass combining textural and spectral analysis of UAV imagery. Precision Agriculture. 20(3):611-629.

Zhou, X., Zheng, H., Xu, X., He, J., Ge, X., Yao, X., Cheng, T., Zhu, Y., Cao, W., Tian, Y.* , 2017. Predicting grain yield in rice using multi-temporal vegetation indices from UAV-based multispectral and digital imagery, ISPRS Journal of Photogrammetry and Remote Sensing. 130:246-255.

张羽，杨涛，马吉锋，黄宇，郑恒彪，程涛，田永超，朱艳，姚霞*. 数学形态学辅助下基于光谱指数的作物冠层组分分类. 农业工程学报，2022，38(07):163-170.

蔡苇荻，张羽，刘海燕，郑恒彪，程涛，田永超，朱艳，曹卫星，姚霞*. 基于成像高光谱的小麦冠层白粉病早期监测方法.中国农业科学，2022，55(06):1110-1126.

印玉明，王永清，马春晨，郑恒彪，程涛，田永超，朱艳，曹卫星，姚霞*.利用日光诱导叶绿素荧光监测水稻叶片叶绿素含量. 农业工程学报，2021，37(12):169-180.

周萌，韩晓旭，郑恒彪，程涛，田永超，朱艳，曹卫星，姚霞*. 基于参数化和非参数化法的棉花生物量高光谱遥感估算.中国农业科学，2021，54(20):4299-4311.

授权和公示国家发明专利：

1. Method of extracting number of stem and tillering for wheat under field condition，美国发明专利，专利号：US12154290B2，Xia Yao; Tai Guo; Xiaohu Zhang; Yan Zhu; Hengbiao Zheng; Tao Cheng; Yongchao Tian; Weixing Cao; Caili Guo; Yu Zhang; Jifeng Ma; Rui Huang; Jie Zhu; Xu Hong; Chongya Jiang; Dong Zhou.

2. 一种基于无人机RGB影像的小麦物候期实时分类方法，专利号：ZL 202210414686.3，姚霞、周萌、杨涛、刘鹏、郑恒彪、李栋、程涛、朱艳、曹卫星、王雪、郭彩丽、张羽、马吉峰.

3. 一种基于体素分割法向量算法的小麦冠层叶倾角分布自动估算方法。专利号：ZL202410018558.6，姚霞、王永清、郭泰、谷洋洋、郑恒彪、程涛、江冲亚、朱艳、曹卫星.

4. 一种基于日光诱导叶绿素荧光指数的水稻盐胁迫早期定量监测方法。专利号：ZL202211270765.8，姚霞、汪康康、朱杰、印玉明、许鑫文、张正东、程涛、朱艳、曹卫星.

5. 一种田间小麦茎蘖数提取方法。专利号：ZL201910223270.1，姚霞、郭泰、方圆、翟苗苗、张晓、程涛、田永超、朱艳、曹卫星、胡永强、许长军.

6. 一种基于Sentinel-2卫星影像红边波段改进小麦生长早期叶面积指数估算的方法。专利号：ZL202110172049.5，姚霞、李伟、程涛、朱艳、田永超、曹卫星、张羽、马吉峰.

科研项目：

1. 江苏省JMRH办，江苏省天空地一体化战场目标智能监测JMRH创新平台，2024.01-2027.12，100万元，项目负责人，在研；

2. 国家自然基金面上项目，基于三维图谱构建小麦理想株型高光效指标及适宜阈值研究，2025.01-2028.12，50万元，项目负责人，在研；

3. 国家重点研发计划课题，地块级农作物高精度产量品质智能测报研究，2022.07-2027.06，375万元，课题负责人，在研；

4. 国家重点研发计划子课题，稻麦产量品质近地面高精度测报，2022.07-2027.06，115万元，子课题负责人，在研；

5. 国家自然基金面上项目，基于日光诱导叶绿素荧光估测高温干旱下小麦生产力机理和方法研究，2020.01-2023.12，58万元，项目负责人，已结题；

6. 国防科工委国家重点研发计划，荧光超光谱探测仪及应用技术，2022.01-2024.12，80万元，课题负责人，已结题；

7. 国家重点研发课题，数据驱动的作物单产遥感监测方法研究，2020.12-2023.11，170.6万元，课题负责人，已结题； 

8. 省自主创新项目，小麦表型高通量获取与智能解译技术及育种应用，2022.07-2024.06，40万元，项目负责人，已结题；

9. 省重点研发计划-现代农业-重点及面上项目，稻麦作物表型高通量获取技术和系统研发，2019.07-2023.06，200万元，项目负责人，已结题；

10. 其他项目（北京威特空间科技有限公司委托项目），基于连续小波分析建立重量定量模型的方法，2022.01-2022.06，27.2万元，项目负责人，已结题；

11. 其他项目（院士咨询项目），农业资源环境监测和信息服务体系发展战略研究，2019.3-2021.3，30万元，项目负责人，已结题。

常年接受从事农业遥感研究的硕士、博士研究生和博士后！尤其欢迎对高光谱影像、日光诱导叶绿素荧光、LiDAR、无人机遥感等感兴趣的同学加盟！欢迎具有遥感与GIS、植物生理学、农业信息学、计算机或测绘工程专业背景的学生报考或申请本团队，优先考虑积极向上，刻苦钻研、勇于探索的学生，男女不限！